{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "%config Completer.use_jedi = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 0, 1, 0, 1, 0, 1, 0],\n",
       " [0, 1, 0, 1, 0, 1, 0, 1],\n",
       " [1, 0, 1, 0, 1, 0, 1, 0],\n",
       " [0, 1, 0, 1, 0, 1, 0, 1],\n",
       " [1, 0, 1, 0, 1, 0, 1, 0],\n",
       " [0, 1, 0, 1, 0, 1, 0, 1],\n",
       " [1, 0, 1, 0, 1, 0, 1, 0],\n",
       " [0, 1, 0, 1, 0, 1, 0, 1]]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row = [0, 1] * 4\n",
    "board = [row[::-1] if i%2 == 1 else row for i in range(1, 9)]\n",
    "board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the .csv file with the preprocessed data.\n",
    "df = pd.read_csv(\"chess_app.csv\", dtype={\"pawns\": int, \"knights\": int, \"bishops\": int,\n",
    "                                         \"rooks\": int, \"queens\": int},\n",
    "                 converters={\"wKing_sqr\": ast.literal_eval, \"bKing_sqr\": ast.literal_eval})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def board_output(vector):\n",
    "\n",
    "    brd = np.zeros((8, 8))\n",
    "    for tup in vector:\n",
    "        brd[tup] += 1\n",
    "\n",
    "    return pd.DataFrame(brd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = board_output(df[\"bKing_sqr\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0      1      2      3       4      5       6      7\n",
      "0  109.0  258.0  616.0  376.0  2494.0  650.0  4186.0  966.0\n",
      "1   78.0  128.0  200.0  337.0   431.0  454.0   640.0  565.0\n",
      "2   81.0   96.0  123.0  201.0   227.0  264.0   222.0  227.0\n",
      "3   53.0   51.0   76.0   97.0   130.0  123.0   106.0  125.0\n",
      "4   49.0   47.0   58.0   72.0    63.0  106.0   112.0   93.0\n",
      "5   51.0   42.0   57.0   51.0    45.0   85.0    70.0   64.0\n",
      "6   36.0   31.0   30.0   26.0    38.0   50.0    23.0   19.0\n",
      "7   21.0   18.0   16.0   23.0    18.0   15.0    20.0   14.0\n"
     ]
    }
   ],
   "source": [
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'A', 2: 'B', 3: 'C', 4: 'D', 5: 'E', 6: 'F', 7: 'G', 8: 'H'}"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_coords = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]\n",
    "replacer = {i+1: x for i, x in enumerate(x_coords)}\n",
    "replacer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.stack().reset_index().rename(columns={\"level_0\":\"rows\",\"level_1\":\"cols\",0:\"freq\"})\n",
    "df2.iloc[:,0:2] = df2.iloc[:,0:2].apply(lambda x:x+1)\n",
    "df2[\"letters\"] = df2.cols.replace(replacer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rows</th>\n",
       "      <th>cols</th>\n",
       "      <th>freq</th>\n",
       "      <th>letters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>109.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>258.0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>616.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>376.0</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2494.0</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>23.0</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>20.0</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>14.0</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    rows  cols    freq letters\n",
       "0      1     1   109.0       A\n",
       "1      1     2   258.0       B\n",
       "2      1     3   616.0       C\n",
       "3      1     4   376.0       D\n",
       "4      1     5  2494.0       E\n",
       "..   ...   ...     ...     ...\n",
       "59     8     4    23.0       D\n",
       "60     8     5    18.0       E\n",
       "61     8     6    15.0       F\n",
       "62     8     7    20.0       G\n",
       "63     8     8    14.0       H\n",
       "\n",
       "[64 rows x 4 columns]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 0,\n",
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       " 0,\n",
       " 1,\n",
       " 1,\n",
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       " 2,\n",
       " 2,\n",
       " 2,\n",
       " 2,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 5,\n",
       " 5,\n",
       " 5,\n",
       " 5,\n",
       " 6,\n",
       " 6,\n",
       " 6,\n",
       " 6,\n",
       " 7,\n",
       " 7,\n",
       " 7,\n",
       " 7]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(list(range(0, 8, 1)) * 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " 1,\n",
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       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
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       " 5,\n",
       " 5,\n",
       " 5,\n",
       " 5,\n",
       " 6,\n",
       " 6,\n",
       " 6,\n",
       " 6,\n",
       " 7,\n",
       " 7,\n",
       " 7,\n",
       " 7]"
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     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "sorted([i for i in range(0,8,1)] * 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = list(range(0,8,2))\n",
    "x_black = []\n",
    "for i in range(8):\n",
    "    if (i % 2) == 0:\n",
    "        x_black += list(range(0,8,2))\n",
    "    else:\n",
    "        x_black += list(range(1,8,2))\n",
    "        \n",
    "x_white = []\n",
    "for i in range(8):\n",
    "    if (i % 2) == 0:\n",
    "        x_white += list(range(1,8,2))\n",
    "    else:\n",
    "        x_white += list(range(0,8,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
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\"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"width\": 500, \"xaxis\": {\"color\": \"white\", \"fixedrange\": true, \"range\": [-0.5, 7.5], \"tickfont\": {\"size\": 12}}, \"yaxis\": {\"color\": \"white\", \"fixedrange\": true, \"range\": [-0.5, 7.5], \"tickfont\": {\"size\": 12}}},                        {\"displayModeBar\": false, \"scrollZoom\": false, \"showAxisDragHandles\": false, \"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('3fd4647e-52ea-479d-b7ef-030a30acaf3d');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "row = [0, 1] * 4\n",
    "boardmatrix = [row[::-1] if i % 2 == 1 else row for i in range(1, 9)]\n",
    "fig = go.Figure(\n",
    "    layout=dict(\n",
    "        margin=dict(l=50, r=50, t=50, b=50, pad=10),\n",
    "        width=500,\n",
    "        height=500,\n",
    "        plot_bgcolor=\"rgba(0,0,0,0)\",\n",
    "        paper_bgcolor=\"rgba(0,0,0,0)\",\n",
    "        font_color=\"white\",\n",
    "        coloraxis_showscale=False,\n",
    "        showlegend=False,\n",
    "        yaxis=dict(range=[-0.5, 7.5], color=\"white\", tickfont_size=12, fixedrange=True),\n",
    "        xaxis=dict(range=[-0.5, 7.5], color=\"white\", tickfont_size=12, fixedrange=True),\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Scatter(\n",
    "        x0=0,\n",
    "        y0=0,\n",
    "        dx=0,\n",
    "        x=x_black,\n",
    "        y=sorted([i for i in range(0,8,1)] * 4),\n",
    "        name=\"Chess Board\",\n",
    "        mode=\"markers\",\n",
    "        opacity=1,\n",
    "        marker_symbol=\"square\",\n",
    "        marker_line_color=\"black\",\n",
    "        marker_size=50,\n",
    "        marker_sizemode=\"diameter\",\n",
    "        marker_opacity=1,\n",
    "        marker_color=\"#303030\",\n",
    "        hoverinfo=\"none\",\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Scatter(\n",
    "        x0=0,\n",
    "        y0=0,\n",
    "        dx=0,\n",
    "        x=x_white,\n",
    "        y=sorted([i for i in range(0,8,1)] * 4),\n",
    "        name=\"Chess Board\",\n",
    "        mode=\"markers\",\n",
    "        opacity=1,\n",
    "        marker_symbol=\"square\",\n",
    "        marker_size=50,\n",
    "        marker_sizemode=\"diameter\",\n",
    "        marker_opacity=1,\n",
    "        marker_color=\"white\",\n",
    "        hoverinfo=\"none\",\n",
    "    )\n",
    ")\n",
    "\n",
    "# return go.Heatmap(\n",
    "# x=list(range(0, 8)),\n",
    "# y=list(range(0, 8)),\n",
    "# x0=0,\n",
    "# y0=0,\n",
    "# dx=0,\n",
    "# z=boardmatrix,\n",
    "# hoverinfo=\"none\",\n",
    "# name=\"Chess Board\",\n",
    "# colorscale=[\"white\", \"#303030\"],\n",
    "# showscale=False,\n",
    "# )\n",
    "\n",
    "\n",
    "fig.show(\n",
    "    config={\"displayModeBar\": False, \"scrollZoom\": False, \"showAxisDragHandles\": False}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_results = {\"Games\": {\"white\": 500, \"black\": 250, \"draw\": 50}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>black</th>\n",
       "      <th>draw</th>\n",
       "      <th>white</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Games</th>\n",
       "      <td>250</td>\n",
       "      <td>50</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "       black  draw  white\n",
       "Games    250    50    500"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dict_results).T"
   ]
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  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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       "data": [
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         "alignmentgroup": "True",
         "hovertemplate": "variable=black<br>value=%{x}<br>index=%{y}<extra></extra>",
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         "marker": {
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dict_results = {\"Games\": {\"white\": 500, \"black\": 250, \"draw\": 50}}\n",
    "fig = px.bar(pd.DataFrame(dict_results).T, orientation=\"h\", barmode=\"stack\", color_discrete_map={\"black\": \"black\", \"white\": \"white\", \"draw\":\"gray\"})\n",
    "margin = 0\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"\",\n",
    "    yaxis_title=\"\",\n",
    "    xaxis_visible=False,\n",
    "    yaxis_visible=False,\n",
    "    plot_bgcolor=\"rgba(0,0,0,0)\",\n",
    "    paper_bgcolor=\"rgba(0,0,0,0)\",\n",
    "    legend_orientation=\"h\",\n",
    "    legend_itemwidth=50,\n",
    "    legend_title=\"\",\n",
    "    legend_xanchor=\"auto\",\n",
    "    legend_x=0.5,\n",
    "    margin=dict(l=margin, r=margin, t=margin, b=margin, pad=0),\n",
    "    legend_font = dict(family=\"Arial\", size=12, color=\"black\"),\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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